Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study

Emilien Arnaud, Mahmoud Elbattah, Christine Ammirati, Gilles Dequen and Daniel Aiham Ghazali ()
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Emilien Arnaud: Department of Emergency Medicine, Amiens Picardy University Hospital, 80000 Amiens, France
Mahmoud Elbattah: Laboratoire Modélisation, Information, Systèmes (MIS), University of Picardie Jules Verne, 80080 Amiens, France
Christine Ammirati: Department of Emergency Medicine, Amiens Picardy University Hospital, 80000 Amiens, France
Gilles Dequen: Laboratoire Modélisation, Information, Systèmes (MIS), University of Picardie Jules Verne, 80080 Amiens, France
Daniel Aiham Ghazali: Laboratoire Modélisation, Information, Systèmes (MIS), University of Picardie Jules Verne, 80080 Amiens, France

IJERPH, 2022, vol. 19, issue 15, 1-13

Abstract: Background: During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy University Hospital (APUH) developed an Artificial Intelligence (AI) project called “Prediction of the Patient Pathway in the Emergency Department” (3P-U) to predict patient outcomes. Materials: Using the 3P-U model, we performed a prospective, single-center study of patients attending APUH’s ED in 2020 and 2021. The objective was to determine the minimum and maximum numbers of beds required in real-time, according to the 3P-U model. Results A total of 105,457 patients were included. The area under the receiver operating characteristic curve (AUROC) for the 3P-U was 0.82 for all of the patients and 0.90 for the unambiguous cases. Specifically, 38,353 (36.4%) patients were flagged as “likely to be discharged”, 18,815 (17.8%) were flagged as “likely to be admitted”, and 48,297 (45.8%) patients could not be flagged. Based on the predicted minimum number of beds (for unambiguous cases only) and the maximum number of beds (all patients), the hospital management coordinated the conversion of wards into dedicated COVID-19 units. Discussion and conclusions: The 3P-U model’s AUROC is in the middle of range reported in the literature for similar classifiers. By considering the range of required bed numbers, the waste of resources (e.g., time and beds) could be reduced. The study concludes that the application of AI could help considerably improve the management of hospital resources during global pandemics, such as COVID-19.

Keywords: COVID-19; artificial intelligence; triage; management of organizations; emergency department (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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